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 [BibTeX] [Marc21]
A Distance Model for Rhythms
Type of publication: Conference paper
Citation: paiement:ICML:2008
Booktitle: 25th International Conference on Machine Learning (ICML)
Year: 2008
Note: IDIAP-RR 08-33
Crossref: paiement:rr08-33:
Abstract: Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of distances between subsequences. A specific implementation of the model when considering Hamming distances over a simple rhythm representation is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy on two different music databases.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Paiement, Jean-Fran├žois
Grandvalet, Yves
Bengio, Samy
Eck, Douglas
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